Which metric is defined as the probability that a positive test result is actually a true positive?

• A. Diagnostic sensitivity
• B. Diagnostic specificity
• C. Positive predictive value
• D. Negative predictive value

Answer: (C)

The main concept tested is positive predictive value: the probability that a person with a positive test actually has the disease. In other words, PPV = true positives divided by all positive results (true positives plus false positives). This makes it the best fit for describing “the probability that a positive test result is actually a true positive.”

Understanding this helps: a positive result is only as trustworthy as the proportion of positives that truly have the disease. Sensitivity asks, if someone has the disease, how often the test is positive—focusing on identifying disease among the diseased. Specificity asks, if someone does not have the disease, how often the test is negative—focusing on identifying non-disease among the healthy. Negative predictive value asks, if the test is negative, what is the probability they do not have the disease—opposite of PPV.

Keep in mind PPV changes with disease prevalence: higher prevalence generally increases the likelihood that a positive result is a true positive.